LAG3, TIM3 and TIGIT: New Targets for Immunotherapy and Potential Associations with Radiotherapy
Bibliographic record
Abstract
The combination of immunotherapy and radiotherapy has demonstrated synergistic potential, especially when a combination of immune checkpoint inhibitors (ICIs) is administered. Cytotoxic T-Lymphocyte-Associated Protein-4 (CTLA-4) inhibitors and Programmed Death-Ligand 1 (PD-L1) inhibitors or Programmed Cell Death Protein 1 (PD-1) inhibitors have been assessed in both clinical and preclinical studies; the addition of radiotherapy activates immunomodulatory mechanisms materialized by an effect similar to "in situ" vaccination or the "abscopal" distant response of lesions outside the irradiation field. The new therapeutic targets (T cell immune-receptor with Ig and ITIM domains (TIGIT), Lymphocyte activating gene 3 (LAG-3), and T cell Ig- and mucin-domain-containing molecule-3 (TIM-3)) associated with traditional ICIs and radiotherapy open new perspectives to the concept of immuno-radiotherapy. The dynamic evaluation of T lymphocyte expression involved in the antitumor immune response, both in the tumor microenvironment (TME) and in the tumor itself, could have biomarker value in assessing the response to combination therapy with traditional and new ICIs in association with irradiation. Preclinical data justify the initiation of clinical trials in various tumor pathologies to explore this concept.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".